A hybrid genetic algorithm based on a two-level hypervolume contribution measure selection strategy for bi-objective flexible job shop problem


Turkyilmaz A., ŞENVAR Ö., Unal I., BULKAN S.

COMPUTERS & OPERATIONS RESEARCH, cilt.141, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 141
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.cor.2021.105694
  • Dergi Adı: COMPUTERS & OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Flexible job shop, Heuristics, Bi-objective, Genetic algorithm, Hypervolume, Tardiness, Local multi-search, Pareto optimality, SCHEDULING PROBLEM, TOTAL TARDINESS, DISPATCHING RULES, TABU SEARCH, INDICATOR, OPTIMIZATION
  • Marmara Üniversitesi Adresli: Evet

Özet

This study addresses the bi-objective flexible job shop problem (BOFJSP) with respect to minimization of the maximum completion time (makespan) and total tardiness. This study aims to propose an algorithm called Biobjective Hybrid Genetic Algorithm - hypervolume contribution measure (BOHGA-HCM) that integrates GA with a multi-search algorithm and uses hypervolume contribution measure (Delta s) in its two-level selection strategy. The initial population is created by randomly assigning operations to the available machines via dispatching rules to find better areas in the search space and enhance diversity to avoid premature convergence. The algorithm handles the objective functions simultaneously with the Pareto Optimality approach. The effectiveness and performance of the proposed algorithm are benchmarked and compared with other algorithms by using well-known data sets presented in the literature.